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Data Scientist Resume Keywords

These are the skills that recent data scientist job postings list most often. We counted them across 559 postings from 395 employers, posted between September 29, 2026 and November 12, 2026.

Top 20 skills in data scientist job postings

  1. 1

    Python

    76% of postings60% as required
  2. 2

    SQL

    61% of postings48% as required
  3. 3

    Machine learning

    36% of postings33% as required
  4. 4

    R

    26% of postings24% as required
  5. 5

    Data visualization

    15% of postings13% as required
  6. 6

    Data science

    15% of postings14% as required
  7. 7

    Spark

    13% of postings10% as required
  8. 8

    scikit-learn

    12% of postings10% as required
  9. 9

    Data analysis

    12% of postings11% as required
  10. 10

    PyTorch

    12% of postings10% as required
  11. 11

    Causal inference

    11% of postings10% as required
  12. 12

    Databricks

    11% of postings8% as required
  13. 13

    Statistical modeling

    10% of postings9% as required
  14. 14

    pandas

    9% of postings7% as required
  15. 15

    Tableau

    9% of postings6% as required
  16. 16

    Statistical analysis

    8% of postings7% as required
  17. 17

    TensorFlow

    8% of postings8% as required
  18. 18

    Snowflake

    8% of postings7% as required
  19. 19

    Statistics

    8% of postings8% as required
  20. 20

    NLP

    8% of postings5% as required

Source: public postings from employer career sites collected by LetMeApply. Skills are read from each posting with AI and merged across spellings (for example, AWS and Amazon Web Services). Each employer counts at most 3 times, so one company hiring in bulk does not skew the list.

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How to use this list

  1. Treat it as a map of the market, not a checklist. Tailor to the posting you are applying to.
  2. For each skill you really have, make sure it is written on your resume, in the posting's wording, where you used it.
  3. Leave out skills you do not have. Listing them can cost you in the interview.